# from functools import partial
#
# # import spconv
# import torch.nn as nn
#
#
# def post_act_block(in_channels, out_channels, kernel_size, indice_key=None, stride=1, padding=0,
#                    conv_type='subm', norm_fn=None):
#
#     if conv_type == 'subm':
#         conv = spconv.SubMConv3d(in_channels, out_channels, kernel_size, bias=False, indice_key=indice_key)
#     elif conv_type == 'spconv':
#         conv = spconv.SparseConv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding,
#                                    bias=False, indice_key=indice_key)
#     elif conv_type == 'inverseconv':
#         conv = spconv.SparseInverseConv3d(in_channels, out_channels, kernel_size, indice_key=indice_key, bias=False)
#     else:
#         raise NotImplementedError
#
#     m = spconv.SparseSequential(
#         conv,
#         norm_fn(out_channels),
#         nn.ReLU(),
#     )
#
#     return m
#
#
# class SparseBasicBlock(spconv.SparseModule):
#     expansion = 1
#
#     def __init__(self, inplanes, planes, stride=1, norm_fn=None, downsample=None, indice_key=None):
#         super(SparseBasicBlock, self).__init__()
#
#         assert norm_fn is not None
#         bias = norm_fn is not None
#         self.conv1 = spconv.SubMConv3d(
#             inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
#         )
#         self.bn1 = norm_fn(planes)
#         self.relu = nn.ReLU()
#         self.conv2 = spconv.SubMConv3d(
#             planes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
#         )
#         self.bn2 = norm_fn(planes)
#         self.downsample = downsample
#         self.stride = stride
#
#     def forward(self, x):
#         identity = x
#
#         out = self.conv1(x)
#         out.features = self.bn1(out.features)
#         out.features = self.relu(out.features)
#
#         out = self.conv2(out)
#         out.features = self.bn2(out.features)
#
#         if self.downsample is not None:
#             identity = self.downsample(x)
#
#         out.features += identity.features
#         out.features = self.relu(out.features)
#
#         return out
#
#
# class VoxelBackBone8x(nn.Module):
#     def __init__(self, model_cfg, input_channels, grid_size, **kwargs):
#         super().__init__()
#         self.model_cfg = model_cfg
#         norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
#
#         self.sparse_shape = grid_size[::-1] + [1, 0, 0]
#
#         self.conv_input = spconv.SparseSequential(
#             spconv.SubMConv3d(input_channels, 16, 3, padding=1, bias=False, indice_key='subm1'),
#             norm_fn(16),
#             nn.ReLU(),
#         )
#         block = post_act_block
#
#         self.conv1 = spconv.SparseSequential(
#             block(16, 16, 3, norm_fn=norm_fn, padding=1, indice_key='subm1'),
#         )
#
#         self.conv2 = spconv.SparseSequential(
#             # [1600, 1408, 41] <- [800, 704, 21]
#             block(16, 32, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv2', conv_type='spconv'),
#             block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
#             block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
#         )
#
#         self.conv3 = spconv.SparseSequential(
#             # [800, 704, 21] <- [400, 352, 11]
#             block(32, 64, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv3', conv_type='spconv'),
#             block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
#             block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
#         )
#
#         self.conv4 = spconv.SparseSequential(
#             # [400, 352, 11] <- [200, 176, 5]
#             block(64, 64, 3, norm_fn=norm_fn, stride=2, padding=(0, 1, 1), indice_key='spconv4', conv_type='spconv'),
#             block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
#             block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
#         )
#
#         last_pad = 0
#         last_pad = self.model_cfg.get('last_pad', last_pad)
#         self.conv_out = spconv.SparseSequential(
#             # [200, 150, 5] -> [200, 150, 2]
#             spconv.SparseConv3d(64, 128, (3, 1, 1), stride=(2, 1, 1), padding=last_pad,
#                                 bias=False, indice_key='spconv_down2'),
#             norm_fn(128),
#             nn.ReLU(),
#         )
#         self.num_point_features = 128
#
#     def forward(self, batch_dict):
#         """
#         Args:
#             batch_dict:
#                 batch_size: int
#                 vfe_features: (num_voxels, C)
#                 voxel_coords: (num_voxels, 4), [batch_idx, z_idx, y_idx, x_idx]
#         Returns:
#             batch_dict:
#                 encoded_spconv_tensor: sparse tensor
#         """
#         voxel_features, voxel_coords = batch_dict['voxel_features'], batch_dict['voxel_coords']
#         batch_size = batch_dict['batch_size']
#         input_sp_tensor = spconv.SparseConvTensor(
#             features=voxel_features,
#             indices=voxel_coords.int(),
#             spatial_shape=self.sparse_shape,
#             batch_size=batch_size
#         )
#
#         x = self.conv_input(input_sp_tensor)
#
#         x_conv1 = self.conv1(x)
#         x_conv2 = self.conv2(x_conv1)
#         x_conv3 = self.conv3(x_conv2)
#         x_conv4 = self.conv4(x_conv3)
#
#         # for detection head
#         # [200, 176, 5] -> [200, 176, 2]
#         out = self.conv_out(x_conv4)
#
#         batch_dict.update({
#             'encoded_spconv_tensor': out,
#             'encoded_spconv_tensor_stride': 8
#         })
#         batch_dict.update({
#             'multi_scale_3d_features': {
#                 'x_conv1': x_conv1,
#                 'x_conv2': x_conv2,
#                 'x_conv3': x_conv3,
#                 'x_conv4': x_conv4,
#             }
#         })
#
#         return batch_dict
#
#
# class VoxelResBackBone8x(nn.Module):
#     def __init__(self, model_cfg, input_channels, grid_size, **kwargs):
#         super().__init__()
#         self.model_cfg = model_cfg
#         norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
#
#         self.sparse_shape = grid_size[::-1] + [1, 0, 0]
#
#         self.conv_input = spconv.SparseSequential(
#             spconv.SubMConv3d(input_channels, 16, 3, padding=1, bias=False, indice_key='subm1'),
#             norm_fn(16),
#             nn.ReLU(),
#         )
#         block = post_act_block
#
#         self.conv1 = spconv.SparseSequential(
#             SparseBasicBlock(16, 16, norm_fn=norm_fn, indice_key='res1'),
#             SparseBasicBlock(16, 16, norm_fn=norm_fn, indice_key='res1'),
#         )
#
#         self.conv2 = spconv.SparseSequential(
#             # [1600, 1408, 41] <- [800, 704, 21]
#             block(16, 32, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv2', conv_type='spconv'),
#             SparseBasicBlock(32, 32, norm_fn=norm_fn, indice_key='res2'),
#             SparseBasicBlock(32, 32, norm_fn=norm_fn, indice_key='res2'),
#         )
#
#         self.conv3 = spconv.SparseSequential(
#             # [800, 704, 21] <- [400, 352, 11]
#             block(32, 64, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv3', conv_type='spconv'),
#             SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res3'),
#             SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res3'),
#         )
#
#         self.conv4 = spconv.SparseSequential(
#             # [400, 352, 11] <- [200, 176, 5]
#             block(64, 128, 3, norm_fn=norm_fn, stride=2, padding=(0, 1, 1), indice_key='spconv4', conv_type='spconv'),
#             SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res4'),
#             SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res4'),
#         )
#
#         last_pad = 0
#         last_pad = self.model_cfg.get('last_pad', last_pad)
#         self.conv_out = spconv.SparseSequential(
#             # [200, 150, 5] -> [200, 150, 2]
#             spconv.SparseConv3d(128, 128, (3, 1, 1), stride=(2, 1, 1), padding=last_pad,
#                                 bias=False, indice_key='spconv_down2'),
#             norm_fn(128),
#             nn.ReLU(),
#         )
#         self.num_point_features = 128
#
#     def forward(self, batch_dict):
#         """
#         Args:
#             batch_dict:
#                 batch_size: int
#                 vfe_features: (num_voxels, C)
#                 voxel_coords: (num_voxels, 4), [batch_idx, z_idx, y_idx, x_idx]
#         Returns:
#             batch_dict:
#                 encoded_spconv_tensor: sparse tensor
#         """
#         voxel_features, voxel_coords = batch_dict['voxel_features'], batch_dict['voxel_coords']
#         batch_size = batch_dict['batch_size']
#         input_sp_tensor = spconv.SparseConvTensor(
#             features=voxel_features,
#             indices=voxel_coords.int(),
#             spatial_shape=self.sparse_shape,
#             batch_size=batch_size
#         )
#         x = self.conv_input(input_sp_tensor)
#
#         x_conv1 = self.conv1(x)
#         x_conv2 = self.conv2(x_conv1)
#         x_conv3 = self.conv3(x_conv2)
#         x_conv4 = self.conv4(x_conv3)
#
#         # for detection head
#         # [200, 176, 5] -> [200, 176, 2]
#         out = self.conv_out(x_conv4)
#
#         batch_dict.update({
#             'encoded_spconv_tensor': out,
#             'encoded_spconv_tensor_stride': 8
#         })
#         batch_dict.update({
#             'multi_scale_3d_features': {
#                 'x_conv1': x_conv1,
#                 'x_conv2': x_conv2,
#                 'x_conv3': x_conv3,
#                 'x_conv4': x_conv4,
#             }
#         })
#
#         return batch_dict
